How to build scalable serverless applications with AWS Lambda 

Netflix changed the binge-watching culture. The digital streaming giant hooked its viewers by releasing entire seasons of its original series all at once, rather than weekly or monthly like other streaming platforms. Apart from a sharp business acumen, Netflix achieved that by utilizing the serverless applications service offered through the technological ingenuity of AWS Lambda. 

AWS Lambda is part of the managed cloud services offered by Amazon that provides serverless computing capabilities. Netflix was one of the earliest adopters of the service because they wanted to automate video encoding and process billions of log events per day. This serverless transformation helped Netflix to quickly process millions of viewing requests and create a hassle-free streaming experience for its audience.    

Serverless computing allows you to build and run applications without managing servers, which leads to more agile development cycles, reduced infrastructure management costs, and improved scalability. AWS Lambda is at the heart of this transformation, providing a robust platform for building scalable and efficient serverless applications. 

In this blog, we will dive deep into how to build scalable serverless applications with AWS Lambda. We will also explore what serverless computing is, how AWS Lambda fits into the serverless model, and how to use  Lambda to build applications that are highly scalable and cost-efficient. 

Understanding serverless computing 

If you know anything about building customized software, you will know that code needs servers to run. However, in the cloud world, serverless doesn’t mean there are no servers. It means developers just don’t have to manage them.  

Serverless computing abstracts managing servers from cloud application development. Instead of provisioning infrastructure, configuring environments, and worrying about scaling, developers simply write code. Cloud providers like AWS, Azure, and Google Cloud take care of your cloud infrastructure management.  

AWS Lambda was the first commercially available serverless compute service that was launched in 2014. Ever since then, different cloud providers have released their own serverless cloud managed services. 

What is AWS Lambda? 

AWS Lambda is a serverless compute service provided by Amazon Web Services (AWS) that allows you to run code in response to events without provisioning or managing servers. With Lambda, you only pay for the compute time your code consumes, and it automatically scales depending on the volume of requests.  

This means you don’t need to worry about server management, capacity planning, or scaling your cloud infrastructure. Lambda takes care of these aspects for you. 

Key Features of AWS Lambda: 

1. Event-driven: Lambda is triggered by events such as HTTP requests via API Gateway, changes to data in Amazon S3, modifications in DynamoDB, or even custom events from other AWS services. 
2. Automatic Scaling: AWS Lambda automatically scales your application by running code in parallel in response to multiple events. 
3. Flexible Languages: Lambda supports several programming languages, including Python, Node.js, Java, Go, .NET Core, and custom runtimes. 
4. Pay-per-Use Pricing: You only pay for the compute power you use, specifically the time your function runs (measured in milliseconds), making Lambda highly cost-efficient. 
5. Provisioned Concurrency: AWS Lambda ensures that your functions are always ready to respond immediately. Provisioned concurrency prevents cold starts by keeping a set of Lambda instances ready to serve requests when needed. 
6. Built-in Security: Lambda has built-in cloud security solutions that help in cloud threat detection and protection. All of its code runs in Amazon Virtual Private Cloud and it has code signing features, which means only the code approved by developers is deployed. 

How to build a scalable serverless application with AWS Lambda 

Using AWS Lambda for serverless cloud application development is a great way to build modern, scalable applications that can deliver results even under heavy traffic.  

Here is a step-by-step approach to building serverless applications with AWS Lambda: 

Step 1: Define the Use Case and Application Architecture 

Identify the core components of your application and determine what services will interact with AWS Lambda. For example: 

• If you’re building a serverless API, you might use Lambda functions triggered by HTTP requests through API Gateway. 
• If you’re processing files uploaded to S3, Lambda functions can be triggered by S3 events. 
• If you need to store application data, you might use DynamoDB in combination with Lambda. 

Step 2: Create Lambda Functions 

Create Lambda functions to handle the application logic. For each Lambda function: 

1. Write the function code using the AWS Lambda console or use your local development environment and deploy the function using AWS CLI or SDKs. 
2. Set up the necessary triggers, such as an API Gateway, S3, or DynamoDB Streams. 
3. Define the function’s IAM role, ensuring it has the appropriate permissions to access other AWS resources like DynamoDB, S3, or SNS. 

Step 3: Set Up an API Gateway (Optional) 

If you are building an API, create RESTful APIs using Amazon API Gateway to expose Lambda functions as HTTP endpoints. Configure methods, authentication, and authorization for your API. 

Step 4: Integrate Event Sources 

Integrate other AWS services, such as S3, DynamoDB, or SNS, to trigger your Lambda functions based on specific events. For example: 

• Set up an S3 event notification to trigger a Lambda function when a file is uploaded to an S3 bucket. 
• Use DynamoDB Streams to trigger Lambda functions when a new item is added to your database. 

Step 5: Monitor and Optimize 

Use Amazon CloudWatch to monitor the performance and health of your Lambda functions. Set up alarms for error rates and function timeouts, and optimize your functions by reviewing logs and tweaking the code to reduce execution time and cost. 

Step 6: Scale as Needed 

AWS Lambda scales automatically based on the volume of incoming requests or events. As traffic increases, Lambda will handle scaling, so you don’t need to worry about capacity planning. 

Why Choose AWS Lambda for Scalable Applications? 

AWS Lambda was the first managed cloud service that made serverless computing popular. And a decade later it still is the leading choice of developers to make highly efficient cloud applications that can meet modern business needs. 

That is because of its many benefits that make cloud infrastructure management smooth and easy. Here are some of those most beneficial advantages: 

1. Automatic Scaling: AWS Lambda automatically scales to handle increasing workloads without any manual intervention. As the number of requests or events increases, Lambda can run multiple instances of your function in parallel, ensuring that your application can handle high traffic and burst traffic seamlessly. 
2. Cost-Effective: With Lambda, you pay only for the compute time you use. This eliminates the need to provision and maintain idle servers, leading to significant cost savings, especially for applications with variable traffic or unpredictable workloads. 
3. No Infrastructure Management: Lambda abstracts away infrastructure management, meaning you don’t need to worry about setting up servers, monitoring health, applying patches, or handling scaling concerns. This frees up your team to focus on application logic and functionality. 
4. Improved Developer Productivity: Lambda enables you to focus on writing code for your application’s business logic instead of managing infrastructure. It integrates well with other AWS services, allowing you to quickly build and deploy scalable applications. 
5. Reliability and High Availability: AWS Lambda is highly available and fault-tolerant. AWS automatically replicates Lambda functions across multiple Availability Zones (AZs) within a region, ensuring that your application remains highly available. 

Key components for building scalable serverless applications 

AWS Lambda works together with other AWS services to deliver high-quality serverless applications. Building a scalable serverless application with AWS Lambda typically involves the following components: 

1. AWS Lambda Functions 

A Lambda function is a small unit of code that runs in response to events. These functions are event-driven, meaning they are triggered by events like HTTP requests, database updates, or changes to files stored in Amazon S3. 

Key considerations: 

• Stateless: Lambda functions are stateless, meaning they don’t maintain any internal state between invocations. If your application requires a state, you can use external services like Amazon DynamoDB or Amazon S3. 
• Timeout: Lambda functions have a maximum execution time (15 minutes per invocation). If your workload requires longer-running tasks, consider breaking them into smaller, discrete tasks or using other AWS services. 

2. Amazon API Gateway

Amazon API Gateway is often used in conjunction with Lambda to build serverless APIs. API Gateway provides a managed environment to create, deploy, and manage APIs that can trigger Lambda functions. 

Key considerations: 

• RESTful API: API Gateway allows you to expose Lambda functions as RESTful APIs. You can configure different HTTP methods (GET, POST, PUT, DELETE) to trigger various Lambda functions. 
• Authorization: API Gateway can integrate with AWS Cognito or other authentication mechanisms to control access to your API. 

3. Amazon S3 (for Event Triggers) 

Amazon S3 is a highly scalable object storage service, often used with Lambda to trigger events. For example, when a file is uploaded to a specific S3 bucket, it can trigger a Lambda function to process that file (e.g., image resizing, log processing, etc.). 

Key considerations: 

• Event Notification: S3 can trigger Lambda functions when objects are uploaded, modified, or deleted. This integration helps build workflows that are triggered by file changes in S3. 

4. Amazon DynamoDB (for Data Storage) 

Amazon DynamoDB is a fast and flexible storage type in AWS. It is basically a NoSQL database service that works well with Lambda. You can use DynamoDB to store and retrieve data in your serverless application. 

Key considerations: 

• Low Latency: DynamoDB provides low-latency data access, which is ideal for real-time applications. 
• Integration: Lambda functions can be triggered automatically by DynamoDB Streams, which helps in processing data changes, such as inserting, updating, or deleting items in a DynamoDB table. 

5. Amazon SNS/Amazon SQS (for Event-Driven Messaging) 

Amazon SNS (Simple Notification Service) and Amazon SQS (Simple Queue Service) are services that help in building decoupled and scalable systems by providing messaging and queuing mechanisms. 

Key considerations: 

• Event-Driven: SNS can trigger Lambda functions based on events like notifications, while SQS can trigger Lambda functions to process messages from a queue. 
• Message Buffering: SQS allows you to buffer messages for later processing, which is useful when you want to decouple event producers and consumers. 

6. Amazon CloudWatch 

Amazon CloudWatch is a monitoring service that you can use to log Lambda function invocations, track errors, and analyze performance metrics. 

Key considerations: 

• Logging: CloudWatch logs allow you to debug your Lambda functions by tracking detailed execution logs. 
• Metrics: You can monitor Lambda performance through CloudWatch Metrics, which include invocation counts, durations, and error rates. 

Best practices for building scalable serverless applications with AWS Lambda 

There are several work-arounds and best practices to get the most of AWS Lambda. These tried and tested techniques help you avoid common bottlenecks while using the tool and get optimal results with a smooth user experience.  

1. Keep Functions Small and Focused: AWS Lambda works best with small, single-purpose functions. Break your application logic into smaller components to improve maintainability and scalability. 
2. Use API Gateway Efficiently: Ensure that your API Gateway endpoints are optimized, and take advantage of caching to reduce load on Lambda functions. 
3. Use DynamoDB with Lambda for Low Latency: DynamoDB is perfect for serverless applications as it provides low-latency access to data. Pairing it with Lambda makes your application faster and more responsive. 
4. Error Handling and Retries: Set up error handling and automatic retries for Lambda functions to ensure reliability and minimize data loss. 
5. Optimize Function Duration: AWS Lambda charges based on execution time. Optimize your code to reduce function duration and, in turn, lower costs. 

Conclusion 

Next time you’re watching your favorite shows on Netflix, remember what goes into launching a platform that scales to millions of users. AWS Lambda is the most visible and effective operational implementation of serverless computing. Lambda separates the software from the burden of cloud infrastructure management, which has empowered developers to focus on innovation instead of orchestration.  

Building scalable serverless applications with AWS Lambda enables you to focus on writing application logic rather than managing infrastructure. With automatic scaling, flexible language support, and cost-effective pricing, Lambda provides an ideal platform for modern application development.  

But AWS Lambda is just one part of building successful serverless applications. Integrating Lambda with API Gateway, S3, DynamoDB, and SNS is key to creating highly scalable and efficient apps. Furthermore, you also need the right strategy following the best practices to optimize serverless workloads.  

Xavor’s managed cloud services help you architect, deploy, and optimize your cloud infrastructure with confidence. Our experts are AWS- and Azure-certified and can manage workloads on all major platforms. 

Drop us a line at [email protected] today to book a free consultation session.